Computational performance of a smoothed particle hydrodynamics simulation for shared-memory parallel computing

Abstract

a b s t r a c tThe computational performance of a smoothed particle hydrodynamics (SPH) simulation is investigated for three types of current shared-memory parallel computer devices: many integrated core (MIC) processors, graphics processing units (GPUs), and multi-core CPUs. We are especially interested in efficient shared-memory allocation methods for each chipset, because the efficient data access patterns differ between compute unified device architecture (CUDA) programming for GPUs and OpenMP programming for MIC processors and multi-core CPUs. We first introduce several parallel implementation techniques for the SPH code, and then examine these on our target computer architectures to determine the most effective algorithms for each processor unit. In addition, we evaluate the effective computing performance and power efficiency of the SPH simulation on each architecture, as these are critical metrics for overall performance in a multi-device environment. In our benchmark test, the GPU is found to produce the best arithmetic performance as a standalone device unit, and gives the most efficient power consumption. The multi-core CPU obtains the most effective computing performance. The computational speed of the MIC processor on Xeon Phi approached that of two Xeon CPUs. This indicates that using MICs is an attractive choice for existing SPH codes on multi-core CPUs parallelized by OpenMP, as it gains computational acceleration without the need for significant changes to the source code.

Dual-criteria time stepping for weakly compressible smoothed particle hydrodynamics

“…A wellknown limitation of WCSPH is the computational efficiency, in particular arising when one aims at long time simulations of large scale systems within reasonable computing cost. One type of techniques to alleviate this limitation is applying High Performance Computing (HPC) techniques by using Central Processing Units (CPUs), Graphics Processing Units (GPUs) and Many Integrated Cores (MICs) systems to accelerate the simulations [25,26,27]. Another type of the acceleration techniques is to modify the numerical algorithms to circumvent or optimize the critical computationally expensive operations [28,29,30].…”

A Parallelization Research for FY Satellite Rainfall Estimate Day Knock off Product Algorithm

“…2) Parallel processing performance and computer architecture are closely related, not only depends on the CPU, but also with the system architecture, instruction structure, memory access speed and other factors [19].…”

Time domain simulation of coupled sloshing–seakeeping problems by SPH–FEM coupling

“…Crespo et al 2011;Domingues et al 2013;Herault et al 2010;Nishiura et al 2015). In this paper, the internal flow dynamics is solved using AquaGPUSPH (Cercós-Pita, 2015) and the seakeeping dynamics is solved with SeaFEM (Serván-Camas and García-Espinosa, 2013).…”